Ravichandar

An integral part of human-robot collaboration is the ability ofthe robot to understand and predict human motion. Predicting what the human collaborator will do next is very useful in planning the robot's response. In this paper, an algorithm for early detection and prediction of human activities is presented. For a given sequential task composed of many steps, a long short-term memory (LSTM) recurrent neural network (RNN) model is trained to learn the underlying sequence of steps. The trained network is then used to make predictions about the subsequent steps the human is about to carry out.